# Can risk-reducing medications improve breast cancer prevention in childhood and adolescent cancer survivors? Comparative modeling to inform care > **NIH NIH R01** · BOSTON CHILDREN'S HOSPITAL · 2024 · $621,799 ## Abstract PROJECT SUMMARY/ABSTRACT Survivors of childhood and adolescent cancer (“survivors”) face high risks for early mortality and treatment- related late effects, including subsequent breast cancer. Approximately 30% of female survivors previously treated with chest radiation will develop breast cancer before age 50, a risk similar to BRCA1 mutation carriers. The Children’s Oncology Group recommends early screening for breast cancer (in those who received radiation) starting at age 25, but <15% of survivors are adherent, many because of economic barriers. Risk- reducing medications, such as selective estrogen receptor modulators and aromatase inhibitors, could allow some survivors to avoid breast cancer entirely (vs. early detection with screening and treatment) since these drugs reduce the risk of estrogen receptor positive (ER+) tumors by 50% and are recommended for high-risk women by the U.S. Preventive Services Task Force. However, risk-reducing medications are not currently standard care for high-risk survivors which includes not only those previously treated with radiation but given emerging data, those who were exposed to high doses of anthracycline chemotherapy. The rarity of childhood and adolescent cancer and the long latency needed to capture subsequent cancers limit the feasibility of prospective prevention trials. Addressing all RFA-CA-20-027 priority areas, we propose to use simulation modeling and longitudinal observational data to inform clinical care by evaluating the clinical benefits and harms of risk-reducing medications among childhood and adolescent cancer survivors. We will build on our established collaboration with the Cancer Intervention and Surveillance Modeling Network (CISNET), the Childhood Cancer Survivor Study (CCSS) and the St. Jude Lifetime Cohort Study to: (1) refine two CISNET models to reflect current knowledge on breast cancer risk, screening and prevention among survivors; (2) provide model results in readily accessible online look-up tables summarizing benefits (e.g., avoiding breast cancer), harms (e.g., medication side effects) and costs to women and society of adding risk-reducing medication use for 5 years to screening; and (3) conduct exploratory analyses to assess the impact of risk- reducing medications and screening on outcomes by race. Our proposed research will have high potential to reshape current paradigms for survivorship care, using breast cancer risk-reducing medications as an example for understanding how preventive agents can be incorporated into current survivorship recommendations and practice. This work will also provide a framework to illuminate key elements and intervention points that can guide efforts to minimize disparities. This project is conceptually innovative and clinically important by simultaneously considering proven effective primary cancer prevention medicines together with screening recommendations. This work will be highly translational by providing data to inform clinical ca... ## Key facts - **NIH application ID:** 10892277 - **Project number:** 5R01CA261874-03 - **Recipient organization:** BOSTON CHILDREN'S HOSPITAL - **Principal Investigator:** Jennifer M. Yeh - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $621,799 - **Award type:** 5 - **Project period:** 2022-09-01 → 2027-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10892277 ## Citation > US National Institutes of Health, RePORTER application 10892277, Can risk-reducing medications improve breast cancer prevention in childhood and adolescent cancer survivors? Comparative modeling to inform care (5R01CA261874-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10892277. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*